Multiple Criteria in a Top Gas Recycling Blast Furnace Optimized through a k-Optimality-Based Genetic Algorithm

摘要

A steel plant flow sheet containing a top gas recycling blast furnace is simulated and subjected to multi-objective optimization through an evolutionary approach. A recently proposed k-optimality criterion is used, which allows optimizing a large number of objectives in an evolutionary way, which is difficult to do by other methods. A number of promising optimum results, showing the optimum tradeoffs between several cost factors are identified and analyzed. The results appear to be very significant in the context of CO2 reduction challenges faced by the steel industries today.